The Potential of Spectral Measurements for Identifying Glyphosate Application to Agricultural Fields

Abstract

Glyphosate is one of the most widely used non-selective systemic herbicides, but nowadays its application is controversially discussed. Optical remote sensing techniques might provide a sufficient tool for monitoring glyphosate use. In order to investigate the potential of this technology, a laboratory experiment was set-up using pots with rolled grass sods. Glyphosate-treated plants were compared to drought-stressed and control plants. All pots were frequently measured using a field spectrometer and a hyperspectral-imaging camera. Plant samples were analysed for photosynthetic pigments, polyphenols and dry matter content. Eight selected vegetation indices were calculated from the spectral measurements. The results show that photosynthetic pigments were sensitive to differentiate between control and glyphosate treated plants already 2 days after application. From the vegetation indices, the normalized difference lignin index (NDLI) responded most sensitively followed by indices referring to photosynthetic pigments, namely, the carotenoid reflectance index (CRI-1) and the photochemical reflectance index (PRI). It can be concluded that spectral vegetation indices are, in principal, a suitable proxy to non-destructively monitor glyphosate application on agricultural fields. Further research is needed to verify its applicability under field conditions. An operational monitoring is, however, currently limited by the requirements for temporal and spectral resolution of the satellite sensors.

Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification

“…Visible near infrared spectroscopy can capture the changes in leaf reflectance in time so as to realize the identification of resistance cultivars. However, it should be pointed out that the high dimension of spectral data limits the calculation speed to some extent, while the spectral index is a combination of several bands, which can obtain similar results while reducing the dimension ( Bloem et al, 2020 ). In this work, living plants were used to achieve non-destructive identification, which was different from in vitro leaves reported in previous study ( Zhang et al, 2022a ).…”

Non-Destructive Detection of Asymptomatic Ganoderma boninense Infection of Oil Palm Seedlings Using NIR-Hyperspectral Data and Support Vector Machine

“…The spectral signature of healthy leaves reflects very low reflectance in the red region but a relatively higher reflectance in the NIR region. SR is sensitive to the chlorophyll content and was developed for estimating photosynthetic pigment concentrations in leaves [66]. Meanwhile, NDVI applications discovered that if chlorophyll production was reduced by biotics (e.g., insect damage, disease), there would be increased red reflection and a reduced NDVI value [67,68].…”

Estratégias Terapêuticas Experimentais Para O Tratamento Da Fibrose Pulmonar

“…Por outro lado, células mais finas, tênues e frágeis, exibem menor deposição de lignina, o que pode ser visualizado com testes simples, rápidos, conforme analisado em nosso trabalho. Adicionalmente, essas análises histoquímicas, em microscopia de luz, em seções obtidas com PEG6000, permitiram analisar e verificar de maneira adequada padrões de crescimento e desenvolvimento dos tecidos vasculares e fundamentais em caules de plantas de tabaco (Figura 2 e 3) ( ANDERSSON-GUNNERÅS et al, 2006;BLOEM et al, 2020;FERREIRA et al, 2017).…”